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Title: Gradient-free MCMC methods for dynamic causal modelling

Here, we compare the performance of four gradient-free MCMC samplers (random walk Metropolis sampling, slice-sampling, adaptive MCMC sampling and population-based MCMC sampling with tempering) in terms of the number of independent samples they can produce per unit computational time. For the Bayesian inversion of a single-node neural mass model, both adaptive and population-based samplers are more efficient compared with random walk Metropolis sampler or slice-sampling; yet adaptive MCMC sampling is more promising in terms of compute time. Slice-sampling yields the highest number of independent samples from the target density -- albeit at almost 1000% increase in computational time, in comparison to the most efficient algorithm (i.e., the adaptive MCMC sampler).
Authors:
 [1] ;  [1] ;  [1]
  1. Univ. College London, London (United Kingdom)
Publication Date:
Grant/Contract Number:
AC05-00OR22725
Type:
Published Article
Journal Name:
NeuroImage
Additional Journal Information:
Journal Volume: 112; Journal ID: ISSN 1053-8119
Publisher:
Elsevier
Research Org:
Univ. College London (United Kingdom)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
Language:
English
Subject:
74 ATOMIC AND MOLECULAR PHYSICS
OSTI Identifier:
1241923
Alternate Identifier(s):
OSTI ID: 1344386